Intelligent agent-based perioperative period health education method for old-age fracture patients

Abstract

The invention provides an intelligent agent-based perioperative health education method for an elderly fracture patient, and the method comprises the steps: inputting multi-dimensional target dialogue content in a target interface of an intelligent agent in response to the elderly fracture patient based on a preset input form, analyzing the target dialogue content based on a preset natural language processing model to obtain a corresponding analysis result, generating personalized health education content corresponding to the target dialogue content according to the analysis result in combination with a preset target health education knowledge base, and determining an interaction mode of the elderly fracture patient for the identity category of the elderly fracture patient. And feeding back personalized health education content to the elderly fracture patient in real time based on an interaction mode, acquiring physiological data and feedback data of the elderly fracture patient, adjusting the personalized health education content based on the physiological data and the feedback data, and feeding back the adjusted personalized health education content to the elderly fracture patient in real time.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to an intelligent agent-based perioperative health education method for elderly fracture patients. Background Art

[0002] In the perioperative management of elderly patients with fractures, health education is an important component to improve patients' rehabilitation outcomes and quality of life.

[0003] At present, existing health education mainly includes traditional face-to-face health education, video-based education, health lectures, patient discussions, mobile applications and other forms.

[0004] However, the real-time interactivity and personalization of existing health education are average, information updates lag behind, and it is not user-friendly for elderly fracture patients. Summary of the invention

[0005] In view of this, the purpose of the present application is to provide a perioperative health education method for elderly patients with fractures based on an intelligent agent. The analysis results obtained by parsing the target dialogue content input by the patient in the target interface of the intelligent agent through natural language processing are combined with the target health education knowledge base to generate personalized health education content, and the personalized health education content can be adjusted based on physiological data and feedback data. The real-time interaction between the intelligent agent and the patient is improved, and the health education content is automatically adjusted according to the specific situation of the patient, thereby improving the personalization of the health education content and realizing rapid information update of the health education content. At the same time, by designing a simple interface, it can help elderly patients with fractures to use technology easily, thereby improving the user-friendliness for elderly patients with fractures.

[0006] In a first aspect, an embodiment of the present application provides an agent-based perioperative health education method for elderly fracture patients, which is applied to an agent; the method comprises:

[0007] In response to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, the target dialogue content is parsed based on a preset natural language processing model to obtain a corresponding parsing result; wherein the input form includes at least voice, text and option box; the target interface is a pre-selected and simple-to-design, user-friendly interface;

[0008] Generate personalized health education content corresponding to the target conversation content based on the analysis result and in combination with a preset target health education knowledge base;

[0009] According to the identity category of the elderly fracture patient, determining the interaction mode of the elderly fracture patient, and feeding back the personalized health education content to the elderly fracture patient in real time based on the interaction mode;

[0010] The physiological data and feedback data of the elderly fracture patient are obtained, the personalized health education content is adjusted based on the physiological data and the feedback data, and the adjusted personalized health education content is fed back to the elderly fracture patient in real time.

[0011] In a possible implementation, the target health education knowledge base is obtained by the following steps:

[0012] Constructing a basic health education knowledge base for elderly patients with fractures, and classifying and processing the image information of the basic health education knowledge base; wherein the image information includes graphic information and audio information;

[0013] The classified image information is converted into rule expression knowledge having a target condition structure and a target behavior structure, and the rule expression knowledge is converted into a target health education knowledge base that can be understood by humans.

[0014] In a possible implementation, generating personalized health education content corresponding to the target conversation content based on the analysis result and in combination with a preset target health education knowledge base includes:

[0015] Acquiring the interaction history of the elderly fracture patient, and extracting interaction features that have an impact on the adjustment of health education content based on the interaction history;

[0016] Using a preset machine learning algorithm, a corresponding education model is established based on the interactive features, and learning data of the elderly fracture patient is identified based on the education model;

[0017] Based on the learning data and the interaction method, the personalized health education content is adjusted.

[0018] In a possible implementation, the personalized health education content includes personalized nursing guidance; the method further includes:

[0019] Determine a corresponding assessment questionnaire based on a preset standardized health assessment tool, send the assessment questionnaire to the elderly fracture patient, and obtain the elderly fracture patient's answer to the assessment questionnaire;

[0020] The compliance of the elderly fracture patient with the treatment plan is evaluated based on the answers, and the personalized nursing guidance is adjusted based on the compliance.

[0021] In a possible implementation, the method further includes:

[0022] Based on the physiological data and the compliance status, the standardized health assessment tool is used to quantitatively score the health status of the elderly fracture patients.

[0023] In a possible implementation manner, the target dialogue content includes a target dialogue question; and the method further includes:

[0024] Acquire various data dimensions determined for elderly fracture patients, and classify and identify the target dialogue questions based on the various data dimensions to obtain personalized key dialogue questions;

[0025] Based on the key dialogue questions, and in combination with the preset expert inference algorithm and the target health education knowledge base, a corresponding optimization strategy is determined, and the personalized health education content is adjusted based on the optimization strategy.

[0026] In a possible implementation, before responding to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, the method further includes:

[0027] In response to the elderly fracture patient inputting login information on the target interface of the intelligent body, judging the login information;

[0028] When the user identity corresponding to the login information is identified, it is determined that the elderly fracture patient has successfully logged in.

[0029] In a second aspect, the embodiment of the present application further provides a perioperative health education device for elderly fracture patients based on an intelligent agent, which is applied to an intelligent agent; the device comprises:

[0030] The first acquisition module is used for responding to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, parsing the target dialogue content based on a preset natural language processing model to obtain a corresponding parsing result; wherein the input form includes at least voice, text and option box; the target interface is a pre-selected and simple-designed, user-friendly interface;

[0031] A generation module, used to generate personalized health education content corresponding to the target dialogue content according to the analysis result and in combination with a preset target health education knowledge base;

[0032] A feedback module, for determining the interaction mode of the elderly fracture patient according to the identity category of the elderly fracture patient, and providing real-time feedback of the personalized health education content to the elderly fracture patient based on the interaction mode;

[0033] The adjustment module is used to obtain the physiological data and feedback data of the elderly fracture patient, adjust the personalized health education content based on the physiological data and the feedback data, and feed back the adjusted personalized health education content to the elderly fracture patient in real time.

[0034] In a possible implementation, the target health education knowledge base is obtained by the following steps:

[0035] Constructing a basic health education knowledge base for elderly patients with fractures, and classifying and processing the image information of the basic health education knowledge base; wherein the image information includes graphic information and audio information;

[0036] The classified image information is converted into rule expression knowledge having a target condition structure and a target behavior structure, and the rule expression knowledge is converted into a target health education knowledge base that can be understood by humans.

[0037] In a possible implementation manner, the generating module is specifically used to:

[0038] Acquiring the interaction history of the elderly fracture patient, and extracting interaction features that have an impact on the adjustment of health education content based on the interaction history;

[0039] Using a preset machine learning algorithm, a corresponding education model is established based on the interactive features, and learning data of the elderly fracture patient is identified based on the education model;

[0040] Based on the learning data and the interaction method, the personalized health education content is adjusted.

[0041] In a possible implementation, the personalized health education content includes personalized nursing guidance; the intelligent agent-based perioperative health education device for elderly fracture patients also includes:

[0042] A second acquisition module is used to determine a corresponding assessment questionnaire based on a preset standardized health assessment tool, and send the assessment questionnaire to the elderly fracture patient to obtain the elderly fracture patient's answer to the assessment questionnaire;

[0043] The third acquisition module is used to evaluate the compliance of the elderly fracture patient with the treatment plan based on the answers, and adjust the personalized nursing guidance based on the compliance.

[0044] In a possible implementation, the agent-based perioperative health education device for elderly fracture patients further includes:

[0045] A scoring module is used to quantitatively score the health status of the elderly fracture patient using the standardized health assessment tool based on the physiological data and the compliance status.

[0046] In a possible implementation, the target dialogue content includes a target dialogue question; the agent-based perioperative health education device for elderly fracture patients further includes:

[0047] The fourth acquisition module is used to acquire various data dimensions determined for elderly fracture patients, and classify and identify the target dialogue questions based on the various data dimensions to obtain personalized key dialogue questions;

[0048] A determination module is used to determine a corresponding optimization strategy based on the key dialogue questions, in combination with a preset expert inference algorithm and the target health education knowledge base, and adjust the personalized health education content based on the optimization strategy.

[0049] In a possible implementation, the agent-based perioperative health education device for elderly fracture patients further includes:

[0050] A judgment module, configured to judge the login information in response to the elderly fracture patient inputting login information on the target interface of the intelligent body;

[0051] The login module is used to determine that the elderly fracture patient has successfully logged in when the user identity corresponding to the login information is identified.

[0052] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the agent-based perioperative health education method for elderly fracture patients as described in any one of the first aspects.

[0053] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the agent-based perioperative health education method for elderly fracture patients described in any one of the first aspects are executed.

[0054] An embodiment of the present application provides an agent-based perioperative health education method for elderly fracture patients. In response to the elderly fracture patient inputting multi-dimensional target dialogue content in a preset input form on a target interface of the intelligent agent, the target dialogue content is parsed based on a preset natural language processing model to obtain a corresponding parsing result, and personalized health education content corresponding to the target dialogue content is generated according to the parsing result and in combination with a preset target health education knowledge base. The interaction mode of the elderly fracture patient is determined according to the identity category of the elderly fracture patient, and the personalized health education content is fed back to the elderly fracture patient in real time based on the interaction mode, the physiological data and feedback data of the elderly fracture patient are obtained, the personalized health education content is adjusted based on the physiological data and feedback data, and the adjusted personalized health education content is fed back to the elderly fracture patient in real time. This application uses natural language processing to parse the target dialogue content input by the patient in the target interface of the intelligent body to obtain the analysis results, and combines the target health education knowledge base to generate personalized health education content, and can adjust the personalized health education content based on physiological data and feedback data. Through real-time interaction between the intelligent body and the patient, the interactivity is improved, the health education content is automatically adjusted according to the specific situation of the patient, the personalization of the health education content is improved, and the rapid information update of the health education content is achieved. At the same time, by designing a simple interface, it can help elderly fracture patients easily use technology, thereby improving the user-friendliness for elderly fracture patients.

[0055] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 is a flow chart of an agent-based perioperative health education method for elderly fracture patients according to one embodiment of the present application;

[0058] Figure 2 is a schematic diagram of the target interface of the agent;

[0059] Figure 3 It is a schematic diagram of the health education process for elderly patients with fractures on the intelligent body;

[0060] Figure 4 is a flow chart of an agent-based perioperative health education method for elderly fracture patients according to another embodiment of the present application;

[0061] Figure 5 is a schematic structural diagram of an intelligent agent-based perioperative health education device for elderly fracture patients according to an embodiment of the present application;

[0062] Figure 6 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0063] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0064] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0065] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0066] Considering the perioperative management of elderly patients with fractures, health education is an important part of improving patients' rehabilitation effects and quality of life.

[0067] At present, existing health education mainly includes traditional face-to-face health education, video-based education, health lectures, patient discussions, mobile applications, etc. However, the real-time interactivity and personalization of existing health education are average, the information update lags behind, and it is not user-friendly for elderly fracture patients.

[0068] To address this problem, the present application provides a perioperative health education method for elderly patients with fractures based on an intelligent agent. The analysis results obtained by parsing the target dialogue content input by the patient in the target interface of the intelligent agent through natural language processing are combined with the target health education knowledge base to generate personalized health education content, and the personalized health education content can be adjusted based on physiological data and feedback data. The real-time interaction between the intelligent agent and the patient is improved, and the health education content is automatically adjusted according to the specific situation of the patient, thereby improving the personalization of the health education content and realizing rapid information update of the health education content. At the same time, by designing a simple interface, it can help elderly patients with fractures to use technology easily, thereby improving the usability of the elderly patients with fractures.

[0069] Figure 1 This is a flow chart of a method for perioperative health education of elderly fracture patients based on an intelligent agent according to an embodiment of the present application, which is applied to an intelligent agent. Figure 1 As shown, the agent-based perioperative health education method for elderly fracture patients in the embodiment of the present application may specifically include:

[0070] S101. In response to an elderly fracture patient inputting multi-dimensional target dialogue content in a target interface of an intelligent agent based on a preset input form, the target dialogue content is parsed based on a preset natural language processing model to obtain corresponding parsing results.

[0071] S102: Generate personalized health education content corresponding to the target dialogue content based on the analysis result and in combination with a preset target health education knowledge base.

[0072] S103. Determine the interaction mode of the elderly fracture patients according to their identity categories, and provide real-time feedback of personalized health education content to the elderly fracture patients based on the interaction mode.

[0073] S104, obtaining physiological data and feedback data of the elderly fracture patient, adjusting personalized health education content based on the physiological data and feedback data, and feeding back the adjusted personalized health education content to the elderly fracture patient in real time.

[0074] In the above-mentioned agent-based perioperative health education method for elderly patients with fractures, the analysis results obtained by natural language processing are analyzed by parsing the target dialogue content input by the patient in the target interface of the intelligent agent, and combined with the target health education knowledge base, personalized health education content is generated, and the personalized health education content can be adjusted based on physiological data and feedback data. The real-time interaction between the intelligent agent and the patient is improved, and the health education content is automatically adjusted according to the specific situation of the patient, which improves the personalization of the health education content and realizes the rapid information update of the health education content. At the same time, by designing a simple interface, it can help elderly patients with fractures to use technology easily, thereby improving the user-friendliness for elderly patients with fractures.

[0075] The above exemplary steps of the embodiment of the present application are described below with reference to specific examples:

[0076] S101, in response to an elderly fracture patient inputting multi-dimensional target dialogue content in a target interface of an intelligent agent based on a preset input form, the target dialogue content is parsed based on a preset natural language processing model to obtain a corresponding parsing result.

[0077] In an embodiment of the present application, the input form includes at least voice, text and option boxes, the target interface is a pre-selected simple and user-friendly interface, the intelligent agent is designed through an agile development model and combined with the technology acceptance model (TAM) and the self-efficacy theory (SET), the target dialogue content is health education-related issues, and the multi-dimensional target dialogue content includes at least health problems, symptom records, living habits and educational needs; after the elderly fracture patient inputs the multi-dimensional target dialogue content in the target interface of the intelligent agent based on the preset input form, the intelligent agent parses the target dialogue content based on the natural language processing (NLP) model to obtain the corresponding parsing result for subsequent processing.

[0078] Among them, health problems refer to patients' descriptions of their current health conditions or specific problems they encounter (for example, "How do I deal with postoperative pain?"); symptom records refer to real-time symptoms input by patients (for example, "I feel a little dizzy today"); living habits refer to information on diet, exercise, medication, etc. provided by patients (for example, "When can I get out of bed and move around"); and educational needs refer to patients' specific needs for health education content (such as wanting to learn about rehabilitation exercises, dietary conditioning, etc.).

[0079] In addition, the intelligent agent is designed through an agile development model combined with the technology acceptance model (TAM) and self-efficacy theory (SET), that is, the intelligent agent is designed using an agile development method based on the results of the demand analysis, and the agile development model quickly responds to user needs and feedback to ensure that the system design and functions are continuously optimized to meet the actual needs of clinical applications. Specifically, through the rapid iteration of the agile development model, the system functions are adjusted in a timely manner according to user feedback. In the long-term development, user data will continue to be accumulated, and performance will be gradually improved to better meet the needs of different users. Through real-time feedback from users, health education content can be quickly adjusted and optimized to ensure the relevance and practicality of information.

[0080] For example, an elderly fracture patient can communicate with the agent by inputting the target dialogue content through voice, text or option box, and ask health education related questions, such as preoperative precautions and postoperative rehabilitation exercises. The agent uses natural language processing technology to parse the patient's input. Figure 2As shown, this is the target interface, where the patient enters the target dialogue content in text to communicate with the intelligent agent and obtain the corresponding answer.

[0081] It can be understood that natural language processing is used to understand the natural language information input by patients and ensure the fluency of intelligent interaction. Specifically, it is used to collect data through real-time conversations with patients, learn patients' expressions and needs, and enhance the ability to understand natural language, thereby improving the accuracy of natural language processing models.

[0082] Here, the target interface of this application is to design a simple and user-friendly interface that can help elderly patients use technology easily and improve participation and user-friendliness.

[0083] It should be noted that before responding to the elderly fracture patient inputting the multi-dimensional target dialogue content on the target interface of the intelligent agent based on the preset input form, responding to the elderly fracture patient inputting login information on the target interface of the intelligent agent, the login information is judged; when the user identity corresponding to the login information is identified, it is determined that the elderly fracture patient has successfully logged in. For example, Figure 3 As shown, the user enters login information through the interface, and the system identifies the user's identity to perform login verification.

[0084] Optionally, when parsing the target conversation content, the agent can decompose complex questions into multiple intermediate question answers to increase the interpretability of the answers while reducing the possibility of hallucinations. This is also an application of the thought chain technology.

[0085] S102, generating personalized health education content corresponding to the target dialogue content according to the analysis result and in combination with a preset target health education knowledge base.

[0086] It should be noted that the target health education knowledge base of the present application is obtained by the following steps: constructing a basic health education knowledge base for elderly patients with fractures, and classifying and processing the image information of the basic health education knowledge base; converting the classified image information into rule expression knowledge with target condition structure and target behavior structure, and converting the rule expression knowledge into a target health education knowledge base that can be understood by people. Among them, the image information includes graphic information and audio information; the basic health education knowledge base includes a large number of medical literature, guidelines and cases. Specifically, the graphic, audio and other information in the basic health education knowledge base for elderly patients with fractures constructed by the research team is classified and processed, and the information is converted into a set of rule expression knowledge so that it has an IF (condition) and THEN (behavior) structure. When the relevant rules are met, the information is converted into a target health education knowledge base that can be understood by people. For example, Figure 3 As shown, NLP technology is used to generate personalized health education content.

[0087] In the embodiment of the present application, based on the analysis result obtained in step S101 and in combination with a preset target health education knowledge base, personalized health education content corresponding to the target dialogue content is generated.

[0088] It should be noted that the rich targeted health education knowledge base constructed by integrating a large amount of medical literature, guidelines and cases provides a scientific basis for personalized health education content.

[0089] S103, according to the identity category of the elderly fracture patients, determine the interaction mode of the elderly fracture patients, and provide real-time feedback of personalized health education content to the elderly fracture patients based on the interaction mode.

[0090] In an embodiment of the present application, the identity categories include the elderly fracture patient himself and the relatives and friends of the elderly fracture patient, the interaction methods include at least voice, video and text, the identity categories and the interaction methods correspond one to one, and different identity categories correspond to different interaction methods. For example, if it is the elderly fracture patient himself, you can choose the voice and video interaction methods, if it is the relatives and friends of the elderly fracture patient, you can choose the text interaction method; according to the identity category of the elderly fracture patient, the interaction method of the elderly fracture patient is determined, and based on the interaction method, the personalized health education content generated in step S102 is fed back to the elderly fracture patient in real time.

[0091] Optionally, through speech analysis or emotion detection techniques, the agent could recognize the identity category of the elderly fracture patient and adjust the interaction accordingly to provide more supportive feedback.

[0092] As a result, through real-time interaction between intelligent agents and patients, instant personalized health education content is provided, thereby improving interactivity.

[0093] S104, obtaining physiological data and feedback data of the elderly fracture patient, adjusting personalized health education content based on the physiological data and feedback data, and feeding back the adjusted personalized health education content to the elderly fracture patient in real time.

[0094] In the embodiment of the present application, the physiological data at least includes heart rate and blood pressure, and the feedback data is the feedback information of the elderly fracture patient. The personalized health education content generated in step S102 is adjusted based on the physiological data and the feedback data to obtain the adjusted personalized health education content, and the adjusted personalized health education content is fed back to the elderly fracture patient in real time. For example, Figure 3 As shown, during data collection and analysis, user feedback data and physiological data are collected to update the database, including adjusting personalized health education content.

[0095] As a result, the intelligent agent can receive the patient's physiological data in real time, analyze it based on this data, and adjust the personalized health education content to ensure the appropriateness of the content.

[0096] The agent-based perioperative health education method for elderly fracture patients provided in the embodiment of the present application is responsive to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, parsing the target dialogue content based on a preset natural language processing model to obtain a corresponding parsing result, generating personalized health education content corresponding to the target dialogue content according to the parsing result and in combination with a preset target health education knowledge base, determining the interaction mode of the elderly fracture patient according to the identity category of the elderly fracture patient, and feeding back the personalized health education content to the elderly fracture patient in real time based on the interaction mode, obtaining the physiological data and feedback data of the elderly fracture patient, adjusting the personalized health education content based on the physiological data and feedback data, and feeding back the adjusted personalized health education content to the elderly fracture patient in real time. The agent-based perioperative health education method for elderly fracture patients of the present application uses natural language processing to parse the target dialogue content input by the patient in the target interface of the intelligent agent to obtain the analysis results, and combines the target health education knowledge base to generate personalized health education content, and can adjust the personalized health education content based on physiological data and feedback data. The real-time interaction between the intelligent agent and the patient improves the interactivity, automatically adjusts the health education content according to the patient's specific situation, improves the personalization of the health education content, and realizes the rapid information update of the health education content. At the same time, by designing a simple interface, it can help elderly fracture patients to easily use technology, thereby improving the user-friendliness for elderly fracture patients.

[0097] Furthermore, the interaction of elderly patients with fractures is obtained; the corresponding interactive content is determined based on the interaction, and the interactive content is pushed to the elderly patients with fractures. The interactive content includes health education videos, articles or interactive exercises. Specifically, the intelligent agent can dynamically push relevant health education videos, articles or interactive exercises based on the interaction of elderly patients with fractures to ensure the timeliness and relevance of the content.

[0098] Further, such as Figure 4 As shown, the step S101 in the above embodiment of "generating personalized health education content corresponding to the target dialogue content according to the analysis result and in combination with the preset target health education knowledge base" may specifically include the following steps:

[0099] S401, obtaining the interaction history of elderly fracture patients, and extracting the interaction features that have an impact on the adjustment of health education content based on the interaction history.

[0100] In the embodiment of the present application, the interaction history refers to the patient's interaction history in the intelligent agent's application. The interaction history includes consulting questions, watching videos, and completing exercises. The interaction characteristics include at least age, postoperative recovery progress, and psychological state. The interaction history of elderly fracture patients is obtained, and based on the interaction history, the interaction characteristics that have an impact on the adjustment of health education content are extracted for subsequent processing.

[0101] S402, using a preset machine learning algorithm, establishing a corresponding education model based on the interactive features, and identifying learning data of elderly fracture patients based on the education model.

[0102] In the embodiment of the present application, the learning data includes learning needs and preferences, and the learning data characterizes the learning needs and preferences of elderly patients with fractures in similar situations. A corresponding education model is established based on the interactive features in step S401 using a machine learning algorithm, and the learning data of elderly patients with fractures is identified based on the education model for subsequent processing. It should be noted that the machine learning algorithm analyzes the health status of the patient based on the interaction history and provides personalized rehabilitation advice.

[0103] S403, adjust personalized health education content based on learning data and interaction methods.

[0104] In the embodiment of the present application, the personalized health education content is adjusted based on the learning data obtained in step S402 and the interaction method in the above embodiment.

[0105] Specifically, the patient's interaction history in the application is collected, including the status of consulting questions, watching videos, and completing exercises. Features that affect the adjustment of educational content, such as the patient's age, postoperative recovery progress, and psychological state, are analyzed and extracted. A model is established using a machine learning algorithm to identify the learning needs and preferences of patients in similar situations. By analyzing the patient's interaction methods, it is determined which educational content can best meet their needs. Based on the results of the model analysis, educational content suitable for patients is automatically generated, such as postoperative exercise guidelines and dietary recommendations. The system continuously optimizes the recommended content based on the patient's real-time feedback and health changes to ensure its relevance and applicability.

[0106] In this way, personalized health education is achieved, and the health education content is automatically adjusted according to the patient's specific situation to ensure the relevance and practicality of the information.

[0107] Furthermore, personalized health education content includes personalized nursing guidance. Based on the preset standardized health assessment tool, the corresponding assessment questionnaire is determined, and the assessment questionnaire is sent to the elderly fracture patients to obtain the elderly fracture patients' answers to the assessment questionnaire; based on the answers, the elderly fracture patients' compliance with the treatment plan is evaluated, and personalized nursing guidance is adjusted based on the compliance. Among them, the standardized health assessment tool can be the SF-36 questionnaire, and the compliance status refers to the elderly fracture patients' compliance with the treatment plan. For example, the questionnaire (SF-36 questionnaire) is used to evaluate the compliance of elderly fracture patients with their treatment plan, such as taking medicine on time, performing rehabilitation exercises, etc.

[0108] Optionally, personalized care instructions, or personalized health education content, may be adjusted based on compliance.

[0109] Optionally, personalized nursing guidance is adjusted based on real-time feedback information from elderly fracture patients.

[0110] Furthermore, based on physiological data and compliance, standardized health assessment tools were used to quantify the health status of elderly patients with fractures. Specifically, the compliance of their treatment regimen was assessed through questionnaires, and combined with physiological data, standardized health assessment tools were used to quantify the health status of elderly patients with fractures.

[0111] This enables the evaluation of the treatment effect on patients.

[0112] Furthermore, the target dialogue content includes target dialogue questions; obtaining various data dimensions determined for elderly patients with fractures, and classifying and identifying the target dialogue questions based on the various data dimensions to obtain personalized key dialogue questions; based on the key dialogue questions, and in combination with the preset expert inference algorithm and the target health education knowledge base, determining the corresponding optimization strategy, and adjusting the personalized health education content based on the optimization strategy. Among them, the various data dimensions are questions about the different needs of elderly patients with fractures, such as exercise problems, diet problems, treatment problems, etc., the target dialogue questions are the target dialogue questions input by elderly patients with fractures into the intelligent body, and the optimization strategy is a personalized recommendation strategy for adjusting the personalized health education content.

[0113] Specifically, in machine learning, the various data dimensions collected from elderly patients with fractures are used to classify problems, identify personalized key issues, predict their key issues of concern, and integrate them with expert inference algorithms and the target health education knowledge base to extract corresponding optimization strategies and achieve personalized recommendations.

[0114] Furthermore, the personalized health education content for elderly fracture patients is dynamically updated within a preset period.

[0115] As a result, a dynamic content update mechanism was established to ensure that patients receive the latest personalized health education content and achieve rapid information updates.

[0116] In addition, it should be noted that all data related to the electronic health records and personal information of elderly fracture patients in this application are stored in cloud servers for disaster recovery backup and data security isolation, and are desensitized, with access to independent accounts and intranet servers restricted. The server is equipped with an independent firewall to achieve data security and privacy protection.

[0117] It should also be noted that, by applying retrieval generation enhancement technology, in order to fully protect patient privacy, patient personal data is stored in the agent's proprietary database, which can only be accessed by medical staff and intelligent agents. When answering questions, the intelligent agent combines general medical industry knowledge and patient personal data to improve the quality of answers while fully protecting patient privacy.

[0118] In general, existing health education programs mostly use unified educational content, while the intelligent agent of this application provides personalized health education content by comprehensively considering the patient's health problems, symptom records, living habits, and educational needs. It can effectively adjust to the individual needs of elderly fracture patients. This personalized service not only improves patients' understanding and acceptance of health education content, but also enhances their sense of participation, thereby promoting compliance, and ultimately helping to reduce the occurrence of postoperative complications. In short, this application improves the accuracy of personalized health education content.

[0119] In addition, the interactive feedback of traditional health education methods is generally not timely, while the intelligent system of this application uses natural language processing technology to allow patients to interact with the system in real time and provide feedback. Patients can get instant answers when they encounter questions. This instant feedback mechanism can effectively reduce patients' anxiety about complex medical information, enhance their confidence, and make them more proactive in preoperative preparation, intraoperative management, and postoperative rehabilitation, thereby optimizing the recovery effect. In short, this application enhances interactivity and feedback mechanisms.

[0120] To continue, the intelligent agent of the present application monitors the patient's compliance in real time and continuously motivates the patient to follow nursing instructions. Based on the patient's real-time data, the system can dynamically adjust the educational content in a timely manner to promote the patient's adherence to the treatment plan, thereby effectively reducing the incidence of postoperative complications. That is, the present application improves the patient's compliance, and the patient's compliance directly affects their health outcomes, so the present application also improves health outcomes.

[0121] Moreover, the intelligent body of this application collects a large amount of patient data and uses big data analysis technology to continuously optimize educational content and nursing guidance. This data-driven dynamic optimization can ensure that the educational content is always consistent with the latest medical advances, improve the scientificity and practicality of the system, and provide patients with more accurate guidance. This application realizes data-driven continuous optimization.

[0122] Finally, this application integrates nursing, medicine, information technology, artificial intelligence, etc., and is no longer limited to the perspective of a single discipline, ensuring the scientificity and practicality of the system design. Interdisciplinary collaboration not only improves the overall design quality of the system, but also enhances its applicability, enabling the system to more comprehensively meet the nursing needs of elderly patients during the perioperative period. At the same time, with the intensification of an aging society, the traditional nursing model faces many challenges and urgently needs intelligent transformation. The intelligent system of this application promotes the transformation of nursing services through intelligent means to adapt to the needs of the new era. This transformation not only improves the efficiency and quality of nursing services, but also provides innovative solutions to solve nursing problems in the context of an aging society, thus having important promotion value in the industry.

[0123] Obviously, this application demonstrates significant technical advantages in personalized guidance, interactivity, nursing compliance, data-driven optimization, interdisciplinary collaboration and intelligent transformation. Compared with existing technical solutions, it can not only effectively meet the perioperative care needs of elderly fracture patients, but also provide a solid foundation for improving the overall quality of medical services.

[0124] Figure 5 This is a flow chart of an intelligent agent-based perioperative health education device for elderly fracture patients according to an embodiment of the present application, which is applied to an intelligent agent. Figure 5 As shown, the perioperative health education device 500 for elderly fracture patients based on an intelligent agent in an embodiment of the present application may specifically include:

[0125] The first acquisition module 501 is used to respond to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the intelligent body based on a preset input form, and parse the target dialogue content based on a preset natural language processing model to obtain corresponding analysis results; wherein the input form includes at least voice, text and option box; the target interface is a pre-selected simple and user-friendly interface.

[0126] The generation module 502 is used to generate personalized health education content corresponding to the target dialogue content according to the analysis result and in combination with a preset target health education knowledge base.

[0127] The feedback module 503 is used to determine the interaction mode of the elderly fracture patients according to their identity categories, and to provide real-time feedback of personalized health education content to the elderly fracture patients based on the interaction mode.

[0128] The adjustment module 504 is used to obtain the physiological data and feedback data of the elderly fracture patients, adjust the personalized health education content based on the physiological data and feedback data, and feed back the adjusted personalized health education content to the elderly fracture patients in real time.

[0129] In a possible implementation, the target health education knowledge base is obtained by the following steps:

[0130] Construct a basic health education knowledge base for elderly patients with fractures, and classify and process the image information of the basic health education knowledge base; the image information includes graphic information and audio information;

[0131] The classified image information is converted into rule-expressed knowledge with target condition structure and target behavior structure, and the rule-expressed knowledge is converted into a target health education knowledge base that can be understood by humans.

[0132] In a possible implementation, the generating module is specifically used for:

[0133] Obtain the interaction history of elderly fracture patients, and extract the interaction features that have an impact on the adjustment of health education content based on the interaction history;

[0134] Using the preset machine learning algorithm, a corresponding education model is established based on the interactive features, and the learning data of elderly fracture patients is identified based on the education model;

[0135] Adjust personalized health education content based on learning data and interaction methods.

[0136] In a possible implementation, the personalized health education content includes personalized nursing guidance; the intelligent agent-based perioperative health education device for elderly fracture patients also includes:

[0137] The second acquisition module is used to determine a corresponding assessment questionnaire based on a preset standardized health assessment tool, and send the assessment questionnaire to the elderly fracture patient to obtain the elderly fracture patient's answer to the assessment questionnaire;

[0138] The third acquisition module is used to evaluate the compliance of elderly fracture patients with the treatment plan based on the answers and adjust personalized nursing guidance based on the compliance.

[0139] In a possible implementation, the intelligent agent-based perioperative health education device for elderly fracture patients further includes:

[0140] A scoring module is used to quantify the health status of elderly patients with fractures using standardized health assessment tools based on physiological data and compliance.

[0141] In a possible implementation, the target dialogue content includes a target dialogue question; and the agent-based perioperative health education device for elderly fracture patients further includes:

[0142] The fourth acquisition module is used to obtain various data dimensions determined for elderly fracture patients, and classify and identify target dialogue questions based on the various data dimensions to obtain personalized key dialogue questions;

[0143] The determination module is used to determine the corresponding optimization strategy based on key dialogue questions and in combination with the preset expert inference algorithm and the target health education knowledge base, and adjust the personalized health education content based on the optimization strategy.

[0144] In a possible implementation, the agent-based perioperative health education device for elderly fracture patients further includes:

[0145] A judgment module, for judging the login information in response to the elderly fracture patient inputting the login information on the target interface of the intelligent agent;

[0146] The login module is used to determine that the elderly fracture patient has successfully logged in when the user identity corresponding to the login information is identified.

[0147] The intelligent agent-based perioperative health education device for elderly fracture patients provided in the embodiment of the present application is capable of enabling elderly fracture patients to input multi-dimensional target dialogue content on the target interface of the intelligent agent based on a preset input form, parsing the target dialogue content based on a preset natural language processing model to obtain corresponding parsing results, generating personalized health education content corresponding to the target dialogue content based on the parsing results and in combination with a preset target health education knowledge base, determining the interaction mode of the elderly fracture patients according to their identity categories, and providing real-time feedback of the personalized health education content to the elderly fracture patients based on the interaction mode, obtaining physiological data and feedback data of the elderly fracture patients, adjusting the personalized health education content based on the physiological data and feedback data, and providing real-time feedback of the adjusted personalized health education content to the elderly fracture patients. The intelligent agent-based perioperative health education device for elderly fracture patients of the present application uses natural language processing to parse the target dialogue content input by the patient in the target interface of the intelligent agent to obtain the analysis result, and combines it with the target health education knowledge base to generate personalized health education content, and can adjust the personalized health education content based on physiological data and feedback data. The real-time interaction between the intelligent agent and the patient improves the interactivity, automatically adjusts the health education content according to the patient's specific situation, improves the personalization of the health education content, and realizes the rapid information update of the health education content. At the same time, by designing a simple interface, it can help elderly fracture patients to easily use technology, thereby improving the user-friendliness for elderly fracture patients.

[0148] like Figure 6As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the above-mentioned agent-based perioperative health education method for elderly fracture patients.

[0149] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memory and processor, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned agent-based perioperative health education method for elderly fracture patients.

[0150] Corresponding to the above-mentioned agent-based perioperative health education method for elderly patients with fractures, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned agent-based perioperative health education method for elderly patients with fractures are executed.

[0151] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0152] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0153] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the deployment method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0154] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An agent-based perioperative health education method for elderly fracture patients, applied to an agent; characterized in that: The method comprises: In response to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, the target dialogue content is parsed based on a preset natural language processing model to obtain a corresponding parsing result; wherein the input form includes at least voice, text and option box; the target interface is a pre-selected and simple-to-design, user-friendly interface; Generate personalized health education content corresponding to the target conversation content based on the analysis result and in combination with a preset target health education knowledge base; According to the identity category of the elderly fracture patient, determining the interaction mode of the elderly fracture patient, and feeding back the personalized health education content to the elderly fracture patient in real time based on the interaction mode; The physiological data and feedback data of the elderly fracture patient are obtained, the personalized health education content is adjusted based on the physiological data and the feedback data, and the adjusted personalized health education content is fed back to the elderly fracture patient in real time.

2. The method according to claim 1, characterized in that The target health education knowledge base is obtained by the following steps: Constructing a basic health education knowledge base for elderly patients with fractures, and classifying and processing the image information of the basic health education knowledge base; wherein the image information includes graphic information and audio information; The classified image information is converted into rule expression knowledge having a target condition structure and a target behavior structure, and the rule expression knowledge is converted into a target health education knowledge base that can be understood by humans.

3. The method according to claim 1, characterized in that The generating of personalized health education content corresponding to the target conversation content according to the analysis result and in combination with a preset target health education knowledge base includes: Acquiring the interaction history of the elderly fracture patient, and extracting interaction features that have an impact on the adjustment of health education content based on the interaction history; Using a preset machine learning algorithm, a corresponding education model is established based on the interactive features, and learning data of the elderly fracture patient is identified based on the education model; Based on the learning data and the interaction method, the personalized health education content is adjusted.

4. The method according to claim 1, characterized in that The personalized health education content includes personalized nursing guidance; the method also includes: Determine a corresponding assessment questionnaire based on a preset standardized health assessment tool, send the assessment questionnaire to the elderly fracture patient, and obtain the elderly fracture patient's answer to the assessment questionnaire; The compliance of the elderly fracture patient with the treatment plan is evaluated based on the answers, and the personalized nursing guidance is adjusted based on the compliance.

5. The method according to claim 4, characterized in that The method further comprises: Based on the physiological data and the compliance status, the standardized health assessment tool is used to quantitatively score the health status of the elderly fracture patients.

6. The method according to claim 1, characterized in that The target dialogue content includes a target dialogue question; the method further includes: Acquire various data dimensions determined for elderly fracture patients, and classify and identify the target dialogue questions based on the various data dimensions to obtain personalized key dialogue questions; Based on the key dialogue questions, and in combination with the preset expert inference algorithm and the target health education knowledge base, a corresponding optimization strategy is determined, and the personalized health education content is adjusted based on the optimization strategy.

7. The method according to claim 1, characterized in that Before the step of responding to the elderly fracture patient inputting the multi-dimensional target dialogue content in the target interface of the agent based on the preset input form, the method further includes: In response to the elderly fracture patient inputting login information on the target interface of the intelligent body, judging the login information; When the user identity corresponding to the login information is identified, it is determined that the elderly fracture patient has successfully logged in.

8. An agent-based perioperative health education device for elderly fracture patients, applied to an agent; characterized in that: The device comprises: The first acquisition module is used for responding to the elderly fracture patient inputting multi-dimensional target dialogue content in the target interface of the agent based on a preset input form, parsing the target dialogue content based on a preset natural language processing model to obtain a corresponding parsing result; wherein the input form includes at least voice, text and option box; the target interface is a pre-selected and simple-designed, user-friendly interface; A generation module, used to generate personalized health education content corresponding to the target dialogue content according to the analysis result and in combination with a preset target health education knowledge base; A feedback module, for determining the interaction mode of the elderly fracture patient according to the identity category of the elderly fracture patient, and providing real-time feedback of the personalized health education content to the elderly fracture patient based on the interaction mode; The adjustment module is used to obtain the physiological data and feedback data of the elderly fracture patient, adjust the personalized health education content based on the physiological data and the feedback data, and feed back the adjusted personalized health education content to the elderly fracture patient in real time.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the agent-based perioperative health education method for elderly fracture patients as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the agent-based perioperative health education method for elderly fracture patients as described in any one of claims 1 to 7.

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